Enable optimization

Classifier optimization is an optional stage aimed at neural readout methods. It can prune weights, distill a student from a teacher, search architectures, reduce bit width via arcade.nn.quantization, or tune hyperparameters with Optuna. The canonical package is arcade.classifieroptimization. arcade.optimization re-exports the same public API.

Classical baselines ignore these knobs. If your classifiers.run list contains only threshold or scikit-learn methods, leave optimization disabled.

See it in action

pip install -e ".[torch]"
bash scripts/link_paper_data.sh
python examples/run_optimization_demo.py
# or open examples/optimization_usecase.ipynb

Recipe: configs/example_optimization.yaml (small split, few NAS trials).

Flip the switch in YAML

optimization:
  enabled: true
  target_classifiers: [fnn, herqules]
  error_budget: 0.01
  pruning:
    enabled: true
  distillation:
    enabled: false
  quantization:
    enabled: true      # uses arcade.nn.quantization.quantize_model
  nas:
    enabled: true
    n_trials: 8

An empty target_classifiers list means every eligible neural method in the run is considered. error_budget is the allowed accuracy drop relative to the unoptimized baseline.

What runs

When optimization.enabled is true, the pipeline calls run_optimization_stage after classify. Candidates that stay within the error budget form a Pareto set (accuracy vs size / method).

Knob	Implementation	Intent
pruning	PruningOptimizer / stage helpers	Sparse weights within error_budget
distillation	DistillationOptimizer	Student from teacher logits
quantization	arcade.nn.quantization	Lower bit-width representation
nas	NASOptimizer / nn.nas	Architecture search
tuning (top-level)	HyperparameterTuner	Optuna or grid search

Caveats

Do not mix optimized and unoptimized networks in one ranking table without labeling rows. Analytical hardware estimates still do not prove bitstream timing. Full API: Classifier optimization.